Developing models of the cardiovascular system that simulates the dynamic behavior of a virtual patient’s condition is fundamental in the medical domain for predictive outcome and hypothesis generation. These models are usually described through Ordinary Differential Equation (ODE). To obtain a patient-specific representative model, it is crucial to have an accurate and rapid estimate of the hemodynamic model parameters. Moreover, when adequate model parameters are found, the resulting time series of state variables can be clinically used for predicting the response to treatments and for non-invasive monitoring. In the Thesis, we address the parameter estimation or inverse modeling, by solving an optimization problem, which aims at minimizing the error between the model output and the target data. In our case, the target data are a set of user-defined state variables, descriptive of a hospitalized specific patient and obtained from time-averaged state variables. The Thesis proposes a comparison of both state-of-the-art and novel methods for the estimation of the underlying model parameters of a cardiovascular simulator Aplysia. All the proposed algorithms are selected and implemented considering the constraints deriving from the interaction with Aplysia. In particular, given the inaccessibility of the ODE, we selected gradient-free methods, which do not need to estimate numerically the derivatives. Furthermore, we aim at having a small number of iterations and objective function calls, since these importantly impact the speed of the estimation procedure, and thus the applicability of the knowledge gained through the parameters at the bedside. Moreover, the Thesis addresses the most common problems encountered in the inverse modeling, among which are the non-convexity of the objective function and the identifiability problem. To assist in resolving the latter issue an identifiability analysis is proposed, after which the unidentifiable parameters are excluded. The selected methods are validated using heart failure data, representative of different pathologies commonly encountered in Intensive Care Unit (ICU) patients. The results show that the gradient-free global algorithms Enhanced Scatter Search and Particle Swarm estimate the parameters accurately at the price of a high number of function evaluations and CPU time. As such, they are not suitable for bedside applications. Besides, the local algorithms are not suitable to find an accurate solution given their dependency on the initial guess. To solve this problem, we propose two methods: the hybrid, and the prior-knowledge algorithms. These methods, by including prior domain knowledge, can find a good solution, escaping the basin of attraction of local minima and producing clinically significant parameters in a few minutes. / Utveckling av modeller av det kardiovaskulära systemet som simulerar det dynamiska beteendet hos en virtuell patients är grundläggande inom det medicinska området för att kunna förutsäga resultat och generera hypoteser. Dessa modeller beskrivs vanligtvis genom Ordinary Differential Equation (ODE). För att erhålla en patientspecifik representativ modell är det viktigt att ha en exakt och snabb uppskattning av de hemodynamiska modellparametrarna. När adekvata modellparametrar har hittats kan de resulterande tidsserierna av tillståndsvariabler dessutom användas kliniskt för att förutsäga svaret på behandlingar och för icke-invasiv övervakning. I avhandlingen behandlar vi parameteruppskattning eller invers modellering genom att lösa ett optimeringsproblem som syftar till att minimera följande felet mellan modellens utdata och måldata. I vårt fall är måldata en uppsättning användardefinierade tillståndsvariabler som beskriver en specifik patient som är inlagd på sjukhus och som erhålls från tidsgenomsnittliga tillståndsvariabler. I avhandlingen föreslås en jämförelse av befintlinga och nya metoder. för uppskattning av de underliggande modellparametrarna i en kardiovaskulär simulator, Aplysia. Alla föreslagna algoritmer är valts och implementerade med hänsyn tagna till de begränsningar som finnis i simulatorn Aplysia. Med tanke på att ODE är otillgänglig har vi valt gradientfria metoder som inte behöver uppskatta derivatorna numeriskt. Dessutom strävar vi efter att ha få interationer och funktionsanrop eftersom dessa påverkar hastigheten på estimeringen och därmed den kliniska användbartheten vid patientbehandling. Avhandlingen behandlas dessutom de vanligaste problemen vid inversmodellering som icke-konvexitet och identifierbarhetsproblem. För att lösa det sistnämnda problemet föreslås en identifierbarhetsanalys varefter de icke-identifierbara parametrarna utesluts. De valda metoderna valideras med hjälp av data om hjärtsvikt som är representativa för olika patologier som ofta förekommer hos Intensive Care Unit (ICU)-patienter. Resultaten visar att de gradientfria globala algoritmerna Enhanced Scatter Search och Particle Swarm uppskattar parametrarna korrekt till priset av ett stort antal funktionsutvärderingar och processortid. De är därför inte lämpliga för tillämpningar vid sängkanten. Dessutom är de lokala algoritmerna inte lämpliga för att hitta en exakt lösning eftersom de är beroende av den ursprungliga gissningen. För att lösa detta problem föreslår vi två metoder: hybridalgoritmer och algoritmer med förhandsinformation. Genom att inkludera tidigare domänkunskap kan dessa metoder hitta en bra lösning som undviker de lokala minimernas attraktionsområde och producerar kliniskt betydelsefulla parametrar på några minuter.

Beim Designprozess elektromagnetischer Aktoren ist die zuverlässige Bestimmung der zu erwartenden Verluste von großer Bedeutung. Während ohmsche Verluste sehr einfach bestimmt werden können, stellen Eisen-/Hystereseverluste häufig einen Unsicherheitsfaktor dar. Hier sind Herstellerangaben meist nur für einige wenige Arbeitspunkte bei harmonischem Betrieb vorhanden. Für den Einsatz in numerischen Berechnungen bei der Auslegung und Simulation solcher Aktoren ist eine detaillierte Beschreibung der ferromagnetischen Hysterese notwendig. Zu diesem Zweck werden häufig das Jiles-Atherton-Hysteresemodell und dessen Weiterentwicklungen eingesetzt. Aufgrund der Vielzahl an verfügbaren modifizierten Varianten wurde im Rahmen dieses Beitrages zunächst untersucht, welche Modellversionen zueinander kompatibel sind. So wird die Verwendung statischer und dynamischer Hysteresemodelle sowie die jeweilig dazu passende inverse Modellform bei konsistenter Parametrierung ermöglicht. Weiterhin wird die Parameteridentifikation anhand experimentell ermittelter Hysteresekurven für verschiedene Werkstoffe mit Hilfe der Particle-Swarm-Optimization vorgestellt. / The reliable determination of the expected losses is important for the design process of electromagnetic actuators. While resistive losses can be determined very easily, iron/hysteresis losses often represent an uncertainty factor. Manufacturer’s specifications are usually only available for a few operating points with harmonic excitation. A detailed description of the ferromagnetic hysteresis is necessary for the use in numerical calculations in the design and simulation of such actuators. For this purpose, the Jiles-Atherton hysteresis model and its further developments are often used. Due to the large number of available modified variants, at first an examination on which model versions are compatible with each other has been performed. This allows the use of static and dynamic hysteresis models as well as the corresponding inverse model form with consistent parameterization. Furthermore, the parameter identification based on experimentally determined hysteresis curves for different materials is presented using particle swarm optimization.

Optimising task allocation in networked systems helps in utilising available resources. When working with unstable and heterogeneous networks, task scheduling can be used to optimise task completion time, energy efficiency and system reliability. The dynamic nature of networks also means that the optimal schedule is subject to change over time. The heterogeneity and variability in network design also complicate the translation of setups from one network to another. Discrete Particle Swarm Optimisation (DPSO) is a metaheuristic that can be used to find solutions to task scheduling. This thesis will explore how DPSO can be used to optimise job scheduling in an unstable network. The purpose is to find solutions for networks like the ones used on trains. This in turn is done to facilitate trajectory planning calculations. Through the use of an artificial neural network, we estimate job scheduling costs. These costs are then used by our DPSO meta heuristic to explore a solution space of potential scheduling. The results focus on the optimisation of batch sizes in relation to network reliability and latency. We simulate a series of unstable and heterogeneous networks and compare completion time. The baseline comparison is the case where scheduling is done by evenly distributing jobs at fixed sizes. The performance of the different approaches is then analysed with regards to usability in real-life scenarios on vehicles. Our results show a noticeable increase in performance within a wide range of network set-ups. This is at the cost of long search times for the DPSO algorithm. We conclude that under the right circumstances, the method can be used to significantly speed up distributed calculations at the cost of requiring significant ahead of time calculations. We recommend future explorations into DPSO starting states to speed up convergence as well as benchmarks of real-life performance. / Optimering av arbetsfördelning i nätverk kan öka användandet av tillgängliga resurser. I instabila heterogena nätverk kan schemaläggning användas för att optimera beräkningstid, energieffektivitet och systemstabilitet. Då nätverk består av sammankopplade resurser innebär det också att vad som är ett optimalt schema kan komma att ändras över tid. Bredden av nätverkskonfigurationer gör också att det kan vara svårt att överföra och applicera ett schema från en konfiguration till en annan. Diskret Particle Swarm Optimisation (DPSO) är en meta heuristisk metod som kan användas för att ta fram lösningar till schemaläggningsproblem. Den här uppsatsen kommer utforska hur DPSO kan användas för att optimera schemaläggning för instabila nätverk. Syftet är att hitta en lösning för nätverk under liknande begränsningar som de som återfinns på tåg. Detta för att i sin tur facilitera planerandet av optimala banor. Genom användandet av ett artificiellt neuralt nätverk (ANN) uppskattar vi schemaläggningskostnaden. Denna kostnad används sedan av DPSO heuristiken för att utforska en lösningsrymd med potentiella scheman. Våra resultat fokuserar på optimeringen av grupperingsstorleken av distribuerade problem i relation till robusthet och letens. Vi simulerar ett flertal instabila och heterogena nätverk och jämför deras prestanda. Utgångspunkten för jämförelsen är schemaläggning där uppgifter distribueras jämnt i bestämda gruperingsstorlekar. Prestandan analyseras sedan i relation till användbarheten i verkliga scenarion. Våra resultat visar på en signifikant ökning i prestanda inom ett brett spann av nätverkskonfigurationer. Det här är på bekostnad av långa söktider för DPSO algoritmen. Vår slutsats är att under rätt förutsättningar kan metoden användas för att snabba upp distribuerade beräkningar förutsatt att beräkningarna för schemaläggningen görs i förväg. Vi rekommenderar vidare utforskande av DPSO algoritmens parametrar för att snabba upp konvergens, samt undersökande av algoritmens prestanda i verkliga miljöer.

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Hydropower systems often contain complex river systems which cause the simulations and analyses of a hydropower operation to be computationally heavy. The complex river system is referred to as something called a Detailed model. By creating a simpler model, denoted the Equivalent model, the computational issue could be circumvented. The purpose of this Equivalent model is to emulate the results of the Detailed model. This thesis computes the Equivalent model for a large hydropower system using Particle Swarm Optimisation- algorithm, then evaluates the Equivalent model performance. Simulations are performed on ten rivers in Sweden, representing four trading areas for one year, October 2017 – September 2018. Furthermore, the year is divided into Quarterly and Seasonal periods, to investigate whether the Equivalent model changes over time. The Equivalent model performance is evaluated based on the relative power difference and computational time compared to the Detailed model. The relative power difference is 4%23% between Equivalent and Detailed models, depending on the period and trading area, with the computational time can be reduced by more than 90%. Furthermore, the Equivalent model changes over time, suggesting that when the year is divided appropriately, the Equivalent model could perform better. The relative power difference results indicate that the Equivalent model performance can still be improved by dividing the periods more appropriately, other than Quarterly or Seasonal. Nevertheless, the results provide a satisfactory Equivalent model, based on the faster computation time and a reasonable relative power difference. Finally, the Equivalent model could be used as a foundation for further analyses and simulations. / Vattenkraftsystem består ofta av komplexa älvsystem som gör att simuleringar och analyser av vattenkraftens operation blir beräkningsmässigt tunga. Det komplexa älvsystem kallas en Detaljeraded modell. Genom att skapa en enklare modell, betecknas som en Ekvivalent modell, beräkningsproblemen kan kringgås. Syftet med denna Ekvivalenta modell är att emulera resultaten av den komplexa Detaljerade modellen. Detta examensarbete beräknar den Ekvivalenta modellen för ett stort vattenkraftssystem med hjälp av Particle Swarm Optimisation- algorithmen, och utvärderar modellprestandan hos Ekvivalenten. Simuleringar utförs på tio älvar i Sverige, som representerar fyra handelsområden under ett år, från oktober 2017 september 2018. Dessutom är året uppdelat i kvartals- och säsongsperioder för att undersöka om den Ekvivalenta modellen förändras över tid. Denna Ekvivalenta modell utvärderas baserat på den relativa effektskillnaden och beräkningstiden jämfört med den Detaljerade modellen. Den relativa effektskillnaden är 4% 23% mellan de Ekvivalenta och Detaljerade modellerna, beroende på period och handelsområde, och beräkningstiden minskas med mer än 90%. Vidare ändras Ekvivalenta modellen över tiden, vilket tyder på att när året delas upp på rätt sätt kan den Ekvivalenta modellen prestera ännu bättre. De relativa effektskillnaderna indikerar att vissa perioder fortfarande kan förbättras genom att dela upp perioden mer korrekt. Trots allt, förser resultanten en tillfredsställande Ekvivalent modell som har en mer effektiv beräkningstid och rimliga effektskillnader. Slutligen skulle den Ekvivalenta modellen kunna användas som en grund för ytterligare analyser och simuleringar.

Although traditionally Wireless Sensor Network (WSNs) have been regarded as static sensor arrays used mainly for environmental monitoring, recently, its applications have undergone a paradigm shift from static to more dynamic environments, where nodes are attached to moving objects, people or animals. Applications that use WSNs in motion are broad, ranging from transport and logistics to animal monitoring, health care and military. These application domains have a number of characteristics that challenge the algorithmic design of WSNs. Firstly, mobility has a negative effect on the quality of the wireless communication and the performance of networking protocols. Nevertheless, it has been shown that mobility can enhance the functionality of the network by exploiting the movement patterns of mobile objects. Secondly, the heterogeneity of devices in a WSN has to be taken into account for increasing the network performance and lifetime. Thirdly, the WSN services should ideally assist the user in an unobtrusive and transparent way. Fourthly, energy-efficiency and scalability are of primary importance to prevent the network performance degradation. This thesis contributes toward the design of a new hybrid optimization algorithm; ENAMS (Energy optimizatioN Algorithm for Mobile Sensor networks) which is based on the Evolutionary Computation and Swarm Intelligence to increase the life time of mobile wireless sensor networks. The presented algorithm is suitable for large scale mobile sensor networks and provides a robust and energy- efficient communication mechanism by dividing the sensor-nodes into clusters, where the number of clusters is not predefined and the sensors within each cluster are not necessary to be distributed in the same density. The presented algorithm enables the sensor nodes to move as swarms within the search space while keeping optimum distances between the sensors. To verify the objectives of the proposed algorithm, the LEGO-NXT MIND-STORMS robots are used to act as particles in a moving swarm keeping the optimum distances while tracking each other within the permitted distance range in the search space.

This research is focused on the integration problem of process planning and scheduling in steel heat treatment operations environment using artificial intelligent techniques that are capable of dealing with such problems. This work addresses the issues involved in developing a suitable methodology for scheduling heat treatment operations of steel. Several intelligent algorithms have been developed for these propose namely, Genetic Algorithm (GA), Sexual Genetic Algorithm (SGA), Genetic Algorithm with Chromosome differentiation (GACD), Age Genetic Algorithm (AGA), and Mimetic Genetic Algorithm (MGA). These algorithms have been employed to develop an efficient intelligent algorithm using Algorithm Portfolio methodology. After that all the algorithms have been tested on two types of scheduling benchmarks. To apply these algorithms on heat treatment scheduling, a furnace model is developed for optimisation proposes. Furthermore, a system that is capable of selecting the optimal heat treatment regime is developed so the required metal properties can be achieved with the least energy consumption and the shortest time using Neuro-Fuzzy (NF) and Particle Swarm Optimisation (PSO) methodologies. Based on this system, PSO is used to optimise the heat treatment process by selecting different heat treatment conditions. The selected conditions are evaluated so the best selection can be identified. This work addresses the issues involved in developing a suitable methodology for developing an NF system and PSO for mechanical properties of the steel. Using the optimisers, furnace model and heat treatment system model, the intelligent system model is developed and implemented successfully. The results of this system were exciting and the optimisers were working correctly.

This thesis studies the effectiveness of bio-inspired optimization algorithms in controlling adaptive antenna arrays. Smart antennas are able to automatically extract the desired signal from interferer signals and external noise. The angular pattern depends on the number of antenna elements, their geometrical arrangement, and their relative amplitude and phases. In the present work different antenna geometries are tested and compared when their array weights are optimized by different techniques. First, the Genetic Algorithm and Particle Swarm Optimization algorithms are used to find the best set of phases between antenna elements to obtain a desired antenna pattern. This pattern must meet several restraints, for example: Maximizing the power of the main lobe at a desired direction while keeping nulls towards interferers. A series of experiments show that the PSO achieves better and more consistent radiation patterns than the GA in terms of the total area of the antenna pattern. A second set of experiments use the Signal-to-Interference-plus-Noise-Ratio as the fitness function of optimization algorithms to find the array weights that configure a rectangular array. The results suggest an advantage in performance by reducing the number of iterations taken by the PSO, thus lowering the computational cost. During the development of this thesis, it was found that the initial states and particular parameters of the optimization algorithms affected their overall outcome. The third part of this work deals with the meta-optimization of these parameters to achieve the best results independently from particular initial parameters. Four algorithms were studied: Genetic Algorithm, Particle Swarm Optimization, Simulated Annealing and Hill Climb. It was found that the meta-optimization algorithms Local Unimodal Sampling and Pattern Search performed better to set the initial parameters and obtain the best performance of the bio-inspired methods studied.

Petroleum Development Oman (PDO) and Oman Electricity Transmission Company (OETC) are running the main 132kV power transmission grids in the Sultanate of Oman. In the year 2001, PDO and OETC grids were interconnected with a 132kV Over head transmission line linking Nahada 132kV substation at PDO's side to Nizwa 132kV sub-station at OETC's side. Since then the power exchange between PDO and OETC is driven by the natural impedances of the system and the frequency and power exchange is controlled by manually re-dispatching the generators. In light of the daily load profile and the forecasted Gulf Cooperation Council (GCC) states electrical interconnection, it is a great challenge for PDO and OETC grids operators to maintain the existing operation philosophy. The objective of this research is to investigate Automatic Generation Control (AGC) technology as a candidate to control the grid frequency and the power exchange between PDO and OETC grid. For this purpose, a dynamic power system model has been developed to represent PDO-OETC interconnected power system. The model has been validated using recorded data from the field which has warranted the requirement of refining the model. Novel approaches have been followed during the course of the model refining process which have reduced the modelling error to an acceptable limit. The refined model has then been used to assess the performance of different AGC control topologies. The recommended control topologies have been further improved using sophisticated control techniques like Linear Quadratic Regulator (LQR) and Fuzzy Logic (FL). Hybrid Fuzzy Logic Proportional Integral Derivative (FLPID) AGC controller has produced outstanding results. The FLPID AGC controller parameters have then been optimised using Multidimensional Unconstrained Nonlinear Minimization function (fminsearch) and Particle Swarm Optimisation (PSO) method. The PSO has been proved to be much superior to fminsearch function. The robustness of the LQR, the fminsearch optimized FLPID and the PSO FLPID optimized AGC controllers has been assessed. The LQR robustness found to be slightly better than the FLPID technique. However the FLPID supercedes the LQR due to the limited number of field feedback signals in comparison to the LQR. Finally, a qualitative assessment of the benefits of the ongoing GCC interconnection project on PDO and OETC has been done through modelling approach. The results proved that the GCC interconnection will bring considerable benefits to PDO and OETC but the interconnection capacity between PDO and OETC needs to be enhanced. However, the application of AGC on PDO and OETC will alleviate the PDO-OETC interconnection capacity enhancement imposed by the GCC interconnection.

This thesis discusses clustering related works with emphasis on Particle Swarm Optimization (PSO) principles. Specifically, we review in detail the PSO clustering algorithm proposed by Van Der Merwe & Engelbrecht, the particle swarm clustering (PSC) algorithm proposed by Cohen & de Castro, Szabo’s modified PSC (mPSC), and Georgieva & Engelbrecht’s Cooperative-Multi-Population PSO (CMPSO). In this thesis, an improvement over Van Der Merwe & Engelbrecht’s PSO clustering has been proposed and tested for standard datasets. The improvements observed in those experiments vary from slight to moderate, both in terms of minimizing the cost function, and in terms of run time.